1. Field of the Invention
Apparatuses and methods consistent with the present invention relate to an electronic adaptive filter for use in processing a communications signal to suppress the effects of interference, and more specifically to an electronic adaptive filter for use in processing a frequency shift keyed (FSK) and/or amplitude shift keyed (ASK) signal.
2. Description of the Related Art
Published French Patent Applications Nos. 2846814, 2846815, 2846825 and 2859336 describe versions of a highly versatile processing system for robustly demodulating a received FSK signal, even when the received FSK signal is in the presence of coherent interference that may be close in frequency to one of the FSK frequencies. Close frequency coherent interference is a type of interference that can severely affect the ability of a receiver to correctly demodulate a desired FSK signal, especially if the close-frequency interference is of greater power than the received FSK signal. Coherent interference is also referred to herein as a “jammer” (i.e., it can jam the ability of a demodulator to correctly demodulate FSK).
A feature of the systems described in published French Patent Applications Nos. 2846814, 2846815, 2846825 and 2859336 is a digital filter for removing jammer signals. The digital filter is operable in adaptive and non-adaptive modes, depending on whether a signal of interest is detected to be present. The adaptive mode is selected when there is no detected signal of interest. In the adaptive mode, the filter coefficients are re-calculated adaptively, so that the filter adapts to cancel out all coherent components appearing in the signal. The non-adaptive mode is selected in response to detection of a signal of interest. In the non-adaptive mode, the values of the filter coefficients are frozen, such that the filter does not adapt to cancel out the signal of interest. Thus the adaptive mode allows the filter to adapt to cancel out jammers as and when they occur in the changing operating environment of the receiver, so that when a signal of interest is received, the filter is already optimized to cancel the existing interference but not the signal of interest.
A result of this operation is that a computation load of the digital filter is higher in the absence of a signal of interest than when a signal of interest is present. In the absence of a signal of interest, the filter in its adaptive mode performs both adaptation and filter calculations, whereas when a signal of interest is present, the filter in its non-adaptive mode performs only the filter calculations.
The computation load of the filter represents a significant part of the computation load of the receiver. A high computation load requires significant receiver and processing resources, such as more extensive circuitry that occupies valuable die space of an integrated circuit, or higher processor occupation of a digital signal processor (DSP), or a faster DSP processor speed. In all cases, a high computation load also results in increased power consumption from the power supply.
From a design point of view, it is desirable that the receiver be provided with sufficient processing resources to support the computation load for receiving and demodulating a desired signal of interest. A relatively higher computation load when no signal of interest is present means that the receiver has to be provided with more processing resources than those used for reception and demodulation of a signal of interest. Moreover, in a typical application of the receiver, e.g., as a remote control receiver for a vehicle, the receiver will spend a significant portion of its time without receiving a signal of interest, which means that the higher computation load of the adaptive mode of the filter impacts the design, power consumption and performance of the entire receiver.
To illustrate the relative computation loads in the adaptive and non-adaptive modes of the filter, FIG. 1 illustrates an example of the main computational stages of a related art Finite Impulse Response (FIR) implementation of a Wiener filter, and FIG. 2 illustrates a computation load for each computation stage of the exemplary related art filter of FIG. 1, where a value N denotes a number of filter taps implemented. Turning to FIG. 1, the computation stages include an FIR filter computation stage 10 used in both the adaptive and non-adaptive modes, and an adaptive computation stage 12 that is used only in the adaptive mode. The FIR filter computation stage 10 is a main filtering stage at which filter coefficients Ci* are applied at time signal samples S(t−i), and a result of the application is subtracted from the input signal by an adder 18. The adaptive computation stage 12 includes a power computation stage 14, and a filter coefficient computation stage 16. The filter coefficient computation stage 16 is the a stage for adaptive updating of the filter coefficients Ci* using a normalised least minimum square (NLMS) algorithm. The power computation stage 14 computes a normalization factor for the filter coefficient computation stage 16.
In the adaptive mode (no signal of interest), all of the computation stages, i.e., the FIR filter computation stage 10, the adaptive computation stage 12 and the power computation stage 14 and filter coefficient computation stage 16 included therein are performed. For each digital sample input to the filter, the computation load is approximately 2N+1 multiplications. In the non-adaptive mode (signal of interest detected), only the FIR filter computation stage 10 is used, and the computation load is approximately N multiplications. (see FIG. 2). Thus, with the filter implementation of FIG. 1, the computation load in the adaptive mode is roughly double that of the non-adaptive mode.